{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77687225-e4dc-4d2b-8371-6e6b269c22c5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "af5177e2-cd8b-4eb3-b175-ccde2809bc93",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Machine Learning with PyTorch and Scikit-Learn  \n",
    "# -- Code Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaed5d23-1f69-4ef6-96ac-328e0b464400",
   "metadata": {},
   "source": [
    "## Package version checks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34c74f6a-16ea-49ae-ae40-695d79a9b0f3",
   "metadata": {},
   "source": [
    "Add folder to path in order to load from the check_packages.py script:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0e488739-f8ab-4fb5-8790-5bb90b89ad25",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '..')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f550fbd0-78ec-4913-84e2-aef8c51bffed",
   "metadata": {},
   "source": [
    "Check recommended package versions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50c6fe8e-e56c-40e3-ae21-07c57872e2ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] Your Python version is 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:35:11) \n",
      "[Clang 11.1.0 ]\n",
      "[OK] numpy 1.21.5\n",
      "[OK] matplotlib 3.5.1\n",
      "[OK] sklearn 1.0.1\n"
     ]
    }
   ],
   "source": [
    "from python_environment_check import check_packages\n",
    "\n",
    "\n",
    "d = {\n",
    "    'numpy': '1.21.2',\n",
    "    'matplotlib': '3.4.3',\n",
    "    'sklearn': '1.0',\n",
    "}\n",
    "check_packages(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b65f2a05",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Chapter 13: Going Deeper -- the Mechanics of PyTorch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ae32129-b9f5-4edf-bb04-6640c2da522b",
   "metadata": {},
   "source": [
    "**Big thanks and credit to Victor Fomin for creating and helping with the original draft of this section!**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddec26f4",
   "metadata": {},
   "source": [
    "## A short introduction to PyTorch-Ignite (Online Bonus Content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df6ab5c2-018c-4aa3-9a26-3e287bef8845",
   "metadata": {},
   "source": [
    "In this section, we will explore PyTorch-Ignite, a library from the PyTorch ecosystem to help with training and evaluating neural networks in PyTorch flexibly and transparently. \n",
    "\n",
    "\n",
    "**Projects using PyTorch-Ignite**\n",
    "\n",
    "\n",
    "There is a list of research papers with code, blog articles, tutorials, toolkits, and other projects that use PyTorch-Ignite. Notable projects include\n",
    "- Medical Open Network for AI (MONAI) (https://monai.io) \n",
    "- Conversational AI with Transfer Learning (https://github.com/huggingface/transfer-learning-conv-ai) \n",
    "If you are interested in more projects using PyTorch-Ignite, please take a look at the detailed project list here: https://github.com/pytorch/ignite#projects-using-ignite\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10233840-52f3-4c62-86f6-10653c54072e",
   "metadata": {},
   "source": [
    "--- \n",
    "\n",
    "As we saw in previous sections, PyTorch training code usually contains two nested for-loops, one iterating over epochs and one iterating over dataset batches. In addition, we evaluate the model on the training and validation set to track the performance during training. In general, we also would like to create training checkpoints (to resume from one in case of an accidental failure); save the best model; use an experiment tracking system to visualize metrics, predictions, and so forth; and perform other basic tasks. These are the kinds of things that PyTorch-Ignite easily handles for the user while keeping PyTorch-like flexibility. In this sense, PyTorch-Ignite aims to simplify the model training process while promoting best practices. \n",
    "\n",
    "In a few words, PyTorch-Ignite provides \n",
    "- An extremely simple engine and event system (training loop abstraction)\n",
    "- Out-of-the-box metrics to easily evaluate models\n",
    "- Built-in handlers to compose training pipelines, save artifacts, and log parameters and metrics\n",
    "- Distributed training support\n",
    "\n",
    "Additional benefits of using PyTorch-Ignite are \n",
    "- Less code than pure PyTorch while ensuring maximum control and simplicity\n",
    "- More modular code\n",
    "In this section, we will build and train again a classifier for the MNIST dataset that we worked with in the previous section.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df3d5ebf-f5ed-4b7b-9252-a9e38a03c22a",
   "metadata": {},
   "source": [
    "--- \n",
    "\n",
    "**Installing PyTorch-Ignite**\n",
    "\n",
    "The code in the following subsections is based on PyTorch-Ignite version 0.4.6. PyTorch-Ignite can be installed via pip or conda, depending on your preference. For instance, the command for installing PyTorch ignite via pip is as follows:\n",
    "\n",
    "    pip install pytorch-ignite\n",
    "\n",
    "Below, you can find the command for installing PyTorch-Ignite via conda:\n",
    " \n",
    "    conda install ignite -c pytorch\n",
    "\n",
    "For the latest information on installing PyTorch-Ignite, please visit the official documentation at https://pytorch.org/ignite/#installation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7722db5f",
   "metadata": {},
   "source": [
    "### Setting up the PyTorch model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b993ca1-d535-4431-af4e-7fd8d0f0a6c1",
   "metadata": {},
   "source": [
    "First, to set the stage, we will repeat Steps 1, 2, and 3 from the section *Project two - classifying MNIST handwritten digits with minor restructuring*. We define the model, training, and validation datasets, optimizer, and loss function: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "220a5ea0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch \n",
    "import torch.nn as nn \n",
    "from torch.utils.data import DataLoader \n",
    " \n",
    "from torchvision.datasets import MNIST \n",
    "from torchvision import transforms\n",
    " \n",
    " \n",
    "image_path = './' \n",
    "torch.manual_seed(1) \n",
    " \n",
    "transform = transforms.Compose([ \n",
    "    transforms.ToTensor() \n",
    "]) \n",
    " \n",
    " \n",
    "mnist_train_dataset = MNIST( \n",
    "    root=image_path,  \n",
    "    train=True,\n",
    "    transform=transform,  \n",
    "    download=True\n",
    ") \n",
    " \n",
    "mnist_val_dataset = MNIST( \n",
    "    root=image_path,  \n",
    "    train=False,  \n",
    "    transform=transform,  \n",
    "    download=False \n",
    ") \n",
    " \n",
    "batch_size = 64\n",
    "train_loader = DataLoader( \n",
    "    mnist_train_dataset, batch_size, shuffle=True \n",
    ") \n",
    " \n",
    "val_loader = DataLoader( \n",
    "    mnist_val_dataset, batch_size, shuffle=False \n",
    ") \n",
    " \n",
    " \n",
    "def get_model(image_shape=(1, 28, 28), hidden_units=(32, 16)): \n",
    "    input_size = image_shape[0] * image_shape[1] * image_shape[2] \n",
    "    all_layers = [nn.Flatten()]\n",
    "    for hidden_unit in hidden_units: \n",
    "        layer = nn.Linear(input_size, hidden_unit) \n",
    "        all_layers.append(layer) \n",
    "        all_layers.append(nn.ReLU()) \n",
    "        input_size = hidden_unit \n",
    " \n",
    "    all_layers.append(nn.Linear(hidden_units[-1], 10)) \n",
    "    all_layers.append(nn.Softmax(dim=1)) \n",
    "    model = nn.Sequential(*all_layers)\n",
    "    return model \n",
    " \n",
    " \n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    " \n",
    "model = get_model().to(device)\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7207c627-4559-4fe2-bccf-826394c7ead7",
   "metadata": {},
   "source": [
    "As you can see, the code above only relies on PyTorch concepts we that we already introduced previously. Via the `get_model()` function, we define a reusable function to conveniently create a multilayer perceptron with a user-defined number of hidden layers, where each hidden layer is followed by a ReLU activation. The output layer is followed by a Softmax layer.\n",
    "\n",
    "Note that the MNIST dataset does not have a pre-defined validation set split. For simplicity, we assigned the test dataset as the validation set for simplicity. However, if we use the validation set for model selection, it does not provide an unbiased estimate of the model's performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99dcabd0",
   "metadata": {},
   "source": [
    "### Setting up training and validation engines with PyTorch-Ignite"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29a2c594-a796-4621-b600-38c9388bc0e9",
   "metadata": {},
   "source": [
    "When we are done setting up the most important parts, PyTorch-Ignite will handle all other boilerplate code. Next, we have to define a trainer engine by passing our model, optimizer, and loss function to the `ignite.engine.create_supervised_trainer()` function for creating a trainer object that is used to train supervised models conveniently (https://pytorch.org/ignite/generated/ignite.engine.create_supervised_trainer.html). In addition, we create an *evaluator* engine by passing PyTorch-Ignite’s out-of-the-box metrics and the model to the `ignite.engine.create_supervised_evaluator()` function (https://pytorch.org/ignite/generated/ignite.engine.create_supervised_evaluator.html#create-supervised-evaluator): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c972bfa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator\n",
    "from ignite.metrics import Accuracy, Loss\n",
    " \n",
    " \n",
    "trainer = create_supervised_trainer(\n",
    "    model, optimizer, loss_fn, device=device\n",
    ")\n",
    " \n",
    "val_metrics = {\n",
    "    \"accuracy\": Accuracy(),\n",
    "    \"loss\": Loss(loss_fn)\n",
    "}\n",
    " \n",
    "evaluator = create_supervised_evaluator(\n",
    "    model, metrics=val_metrics, device=device\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18d21eb8-0825-4f7f-813b-0f0f5f3f6131",
   "metadata": {},
   "source": [
    "Both the `trainer` and `evaluator` objects are instances of the `Engine` class (https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html#ignite.engine.engine.Engine), which is one of the core components of PyTorch-Ignite. It is essentially an abstraction over the training or validation loops."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa17c608",
   "metadata": {},
   "source": [
    "### Creating event handlers for logging and validation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7cf3cdc-7a35-4a7a-8d36-6feaca029a85",
   "metadata": {},
   "source": [
    "We can customize the code further by adding all kinds of event handlers. `Engine` allows adding handlers for various events that are triggered during the run. When an event is triggered, the attached handlers (functions) are executed. Thus, for logging purposes, we add a function that will be executed at the end of every `log_interval` iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0edafc78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# How many batches to wait before logging training status\n",
    "log_interval = 100\n",
    " \n",
    "@trainer.on(Events.ITERATION_COMPLETED(every=log_interval))\n",
    "def log_training_loss():\n",
    "    e = trainer.state.epoch\n",
    "    max_e = trainer.state.max_epochs\n",
    "    i = trainer.state.iteration\n",
    "    batch_loss = trainer.state.output\n",
    "    print(f\"Epoch[{e}/{max_e}], Iter[{i}] Loss: {batch_loss:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52219744-e34e-4b97-885c-c19ec0e2cd4b",
   "metadata": {},
   "source": [
    "Or, equivalently, without the decorator, we can attach the handler function to the trainer via an `add_event_handler()` call (https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html#ignite.engine.engine.Engine.add_event_handler).\n",
    "\n",
    "Similar to creating an event handler for logging the training status that we created above, we can create an event handler for computing validation metrics after each epoch.\n",
    "Via the following code, we will run the `evaluator` on the validation set data loader, `val_loader`, when an epoch completes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1944b444",
   "metadata": {},
   "outputs": [],
   "source": [
    "@trainer.on(Events.EPOCH_COMPLETED)\n",
    "def log_validation_results():\n",
    "    eval_state = evaluator.run(val_loader)\n",
    "    metrics = eval_state.metrics\n",
    "    e = trainer.state.epoch\n",
    "    max_e = trainer.state.max_epochs\n",
    "    acc = metrics['accuracy']\n",
    "    avg_loss = metrics['loss']\n",
    "    print(f\"Validation Results - Epoch[{e}/{max_e}] Avg Accuracy: {acc:.2f} Avg Loss: {avg_loss:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11b8cdfa",
   "metadata": {},
   "source": [
    "### Setting up training checkpoints and saving the best model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "773a23a4-6536-47d5-8710-05bb1407d2c5",
   "metadata": {},
   "source": [
    "It is a common practice to save the trainer, model, optimizer, and other related objects during the training process. This allows us to resume the model training from the checkpoint in case of an accidental training interruption. We will use an out-of-the-box PyTorch-Ignite handler to set up a training checkpointing for each epoch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e451c03b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ignite.engine.events.RemovableEventHandle at 0x10798ad00>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ignite.handlers import Checkpoint, DiskSaver\n",
    " \n",
    "# We will save in the checkpoint the following:\n",
    "to_save = {\"model\": model, \"optimizer\": optimizer, \"trainer\": trainer}\n",
    " \n",
    "# We will save checkpoints to the local disk\n",
    "output_path = \"./output\"\n",
    "save_handler = DiskSaver(dirname=output_path, require_empty=False)\n",
    " \n",
    "# Set up the handler:\n",
    "checkpoint_handler = Checkpoint(\n",
    "    to_save, save_handler, filename_prefix=\"training\")\n",
    "\n",
    "# Attach the handler to the trainer\n",
    "trainer.add_event_handler(Events.EPOCH_COMPLETED, checkpoint_handler)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73af287b-77b3-43d5-b47d-e9add713d991",
   "metadata": {},
   "source": [
    "Via the code above, we created a `Checkpoint` object (https://pytorch.org/ignite/generated/ignite.handlers.checkpoint.Checkpoint.html#ignite.handlers.checkpoint.Checkpoint), which allows us to save and load a model later.\n",
    "\n",
    "Besides saving models to be able to resume an interrupted training run, we are primarily interested in saving the best model, for example, for making predictions later during the inference stage. We can then load a saved model via `torch.load` as explained in the section Saving and reloading the trained model in *Chapter 12, Parallelizing Neural Network Training with PyTorch*.\n",
    "\n",
    "Usually, the best model is determined by the value of a validation metric. We will use the same handler, `Checkpoint`, to save the best model according to the highest validation accuracy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e3c8def7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ignite.engine.events.RemovableEventHandle at 0x1079bba60>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store best model by validation accuracy\n",
    "best_model_handler = Checkpoint(\n",
    "    {\"model\": model},\n",
    "    save_handler,\n",
    "    filename_prefix=\"best\",\n",
    "    n_saved=1,\n",
    "    score_name=\"accuracy\",\n",
    "    score_function=Checkpoint.get_default_score_fn(\"accuracy\"),\n",
    ")\n",
    " \n",
    "evaluator.add_event_handler(Events.COMPLETED, best_model_handler)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7092b069",
   "metadata": {},
   "source": [
    "### Setting up TensorBoard as an experiment tracking system"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bdcedf6-b4ed-4172-843e-676ca8b65d1e",
   "metadata": {},
   "source": [
    "When running training with different configurations, another common practice is to use an experiment tracking system, for example, TensorBoard, to log parameters and metrics and to compare experiments. We will use the `TensorboardLogger` (https://pytorch.org/ignite/generated/ignite.contrib.handlers.tensorboard_logger.html#ignite.contrib.handlers.tensorboard_logger.TensorboardLogger) to log the trainer's loss and validation metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9dc0368b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ignite.engine.events.RemovableEventHandle at 0x13e0ddf40>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ignite.contrib.handlers import TensorboardLogger, global_step_from_engine\n",
    " \n",
    " \n",
    "tb_logger = TensorboardLogger(log_dir=output_path)\n",
    " \n",
    "# Attach handler to plot trainer's loss every 100 iterations\n",
    "tb_logger.attach_output_handler(\n",
    "    trainer,\n",
    "    event_name=Events.ITERATION_COMPLETED(every=100),\n",
    "    tag=\"training\",\n",
    "    output_transform=lambda loss: {\"batch_loss\": loss},\n",
    ")\n",
    " \n",
    "# Attach handler for plotting both evaluators' metrics after every epoch completes\n",
    "tb_logger.attach_output_handler(\n",
    "    evaluator,\n",
    "    event_name=Events.EPOCH_COMPLETED,\n",
    "    tag=\"validation\",\n",
    "    metric_names=\"all\",\n",
    "    global_step_transform=global_step_from_engine(trainer),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad26fb6b",
   "metadata": {},
   "source": [
    "### Executing the PyTorch-Ignite model training code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4216f020-b381-4880-8a00-dcb01a66524f",
   "metadata": {},
   "source": [
    "The trainer is now set up and ready to be executed. Let’s train the model for five epochs via the `run()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7f4d38cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[1/5], Iter[100] Loss: 1.87\n",
      "Epoch[1/5], Iter[200] Loss: 1.82\n",
      "Epoch[1/5], Iter[300] Loss: 1.67\n",
      "Epoch[1/5], Iter[400] Loss: 1.55\n",
      "Epoch[1/5], Iter[500] Loss: 1.65\n",
      "Epoch[1/5], Iter[600] Loss: 1.59\n",
      "Epoch[1/5], Iter[700] Loss: 1.59\n",
      "Epoch[1/5], Iter[800] Loss: 1.56\n",
      "Epoch[1/5], Iter[900] Loss: 1.63\n",
      "Validation Results - Epoch[1/5] Avg Accuracy: 0.91 Avg Loss: 1.56\n",
      "Epoch[2/5], Iter[1000] Loss: 1.61\n",
      "Epoch[2/5], Iter[1100] Loss: 1.56\n",
      "Epoch[2/5], Iter[1200] Loss: 1.54\n",
      "Epoch[2/5], Iter[1300] Loss: 1.54\n",
      "Epoch[2/5], Iter[1400] Loss: 1.51\n",
      "Epoch[2/5], Iter[1500] Loss: 1.53\n",
      "Epoch[2/5], Iter[1600] Loss: 1.50\n",
      "Epoch[2/5], Iter[1700] Loss: 1.50\n",
      "Epoch[2/5], Iter[1800] Loss: 1.52\n",
      "Validation Results - Epoch[2/5] Avg Accuracy: 0.92 Avg Loss: 1.54\n",
      "Epoch[3/5], Iter[1900] Loss: 1.61\n",
      "Epoch[3/5], Iter[2000] Loss: 1.60\n",
      "Epoch[3/5], Iter[2100] Loss: 1.54\n",
      "Epoch[3/5], Iter[2200] Loss: 1.51\n",
      "Epoch[3/5], Iter[2300] Loss: 1.48\n",
      "Epoch[3/5], Iter[2400] Loss: 1.56\n",
      "Epoch[3/5], Iter[2500] Loss: 1.57\n",
      "Epoch[3/5], Iter[2600] Loss: 1.52\n",
      "Epoch[3/5], Iter[2700] Loss: 1.54\n",
      "Epoch[3/5], Iter[2800] Loss: 1.54\n",
      "Validation Results - Epoch[3/5] Avg Accuracy: 0.93 Avg Loss: 1.53\n",
      "Epoch[4/5], Iter[2900] Loss: 1.53\n",
      "Epoch[4/5], Iter[3000] Loss: 1.49\n",
      "Epoch[4/5], Iter[3100] Loss: 1.51\n",
      "Epoch[4/5], Iter[3200] Loss: 1.51\n",
      "Epoch[4/5], Iter[3300] Loss: 1.54\n",
      "Epoch[4/5], Iter[3400] Loss: 1.50\n",
      "Epoch[4/5], Iter[3500] Loss: 1.58\n",
      "Epoch[4/5], Iter[3600] Loss: 1.59\n",
      "Epoch[4/5], Iter[3700] Loss: 1.50\n",
      "Validation Results - Epoch[4/5] Avg Accuracy: 0.94 Avg Loss: 1.53\n",
      "Epoch[5/5], Iter[3800] Loss: 1.52\n",
      "Epoch[5/5], Iter[3900] Loss: 1.60\n",
      "Epoch[5/5], Iter[4000] Loss: 1.50\n",
      "Epoch[5/5], Iter[4100] Loss: 1.50\n",
      "Epoch[5/5], Iter[4200] Loss: 1.51\n",
      "Epoch[5/5], Iter[4300] Loss: 1.57\n",
      "Epoch[5/5], Iter[4400] Loss: 1.56\n",
      "Epoch[5/5], Iter[4500] Loss: 1.55\n",
      "Epoch[5/5], Iter[4600] Loss: 1.50\n",
      "Validation Results - Epoch[5/5] Avg Accuracy: 0.94 Avg Loss: 1.52\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "State:\n",
       "\titeration: 4690\n",
       "\tepoch: 5\n",
       "\tepoch_length: 938\n",
       "\tmax_epochs: 5\n",
       "\toutput: 1.5042390823364258\n",
       "\tbatch: <class 'list'>\n",
       "\tmetrics: <class 'dict'>\n",
       "\tdataloader: <class 'torch.utils.data.dataloader.DataLoader'>\n",
       "\tseed: <class 'NoneType'>\n",
       "\ttimes: <class 'dict'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.run(train_loader, max_epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a0ba62e-6d9c-4dfd-a679-d67ca16edb23",
   "metadata": {},
   "source": [
    "We can start TensorBoard server with\n",
    "    tensorboard --logdir='./output'\n",
    "and display the dashboard in the browser:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6dd9e080-f294-4314-ac8d-e023d4274d97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='figures/ignite-01.png') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0a89d55-d4a9-4086-b285-0d346641b240",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "**Next steps with PyTorch-Ignite**\n",
    "\n",
    "To learn more about PyTorch-Ignite, please check out the official website containing and tutorials and how-to guides at https://pytorch-ignite.ai.\n",
    "\n",
    "Among others, the website also includes a handy PyTorch-Ignite code-generator application (https://code-generator.pytorch-ignite.ai/) so you can start working on tasks without rewriting everything from scratch.\n",
    "\n",
    "PyTorch-Ignite's code is available on GitHub: https://github.com/pytorch/ignite. The project is a community effort, and everyone is welcome to contribute and join the contributors’ community no matter your background and skills!\n",
    "\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "523acf34",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Readers may ignore the next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "29befadd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] WARNING | Config option `kernel_spec_manager_class` not recognized by `NbConvertApp`.\n",
      "[NbConvertApp] Converting notebook ch13_part4_ignite.ipynb to script\n",
      "[NbConvertApp] Writing 13057 bytes to ch13_part4_ignite.py\n"
     ]
    }
   ],
   "source": [
    "! python ../.convert_notebook_to_script.py --input ch13_part4_ignite.ipynb --output ch13_part4_ignite.py"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
